class: title-slide <br> <br> # Effects of Early Warning Emails on Student Performance <br> .padding_left.pull-down.white[ .bold[_J. Klenke_], T. Massing, N. Reckmann, J. Langerbein, B. Otto, M. Goedicke, C. Hanck <br> <br> <br> `\(15^{TH}\)` International Conference on Computer Supported Education Prague, 21-23 April, 2023 ] --- name: course # Research Idea and Course Description ## Research question .blockquote[ Does objective and motivating feedback through a warning email have a positive impact on student's performance ] -- - Analyzed Course: _Inferential Statistics_ at the University of Duisburg-Essen - Compulsory for business and economics - Weekly 2-hour lecture - Weekly 2-hour exercise - We also have other interventions - __802__ students at the beginning of the semester - __337__ students took an exam at the end of the semester --- # Treatment Assignment <br> <br> - A logit model was used to predict students' probability to pass the exam based on the first 3 online tests - The model was trained with the latest data obtained from the previous edition of the same course -- - If predicted probability to pass `\(\leq 0.4\)` the student got a warning mail ??? - [Kahoot!](https://kahoot.com/) games used during classes - Homework (formative assessment) and 5 online tests (summative assessment) on the e-assessment platform [JACK](https://s3.paluno.uni-due.de/en/forschung/spalte1/e-learning-und-e-assessment) --- # Course Timeline Main Events <br> <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#plots/timeline_plot.png" alt="Timeline for the key events in the 2019 summer term course Inferential Statistics (treatment cohort)" width="80%" /> <p class="caption">Timeline for the key events in the 2019 summer term course Inferential Statistics (treatment cohort)</p> </div> - The shaded area indicates the period after treatment - 57 days between the warning mail and `\(1^{st}\)` exam - 113 days between the warning email and `\(2^{nd}\)` exam --- name: literature # Literature on Warning Systems in Education .font80[ - <a id='cite-Arnold2012'></a><a href='#bib-Arnold2012'>Arnold and Pistilli (2012)</a> investigated the effect of the signal light system at Purdue University and found a positive effect on student grades - <a id='cite-baneres2020'></a><a href='#bib-baneres2020'>Bañeres, Rodríguez, Guerrero-Roldán, and Karadeniz (2020)</a> implemented an early warning system but did not analyze the effect on students' performance - <a id='cite-csahin2019'></a><a href='#bib-csahin2019'>Şahin and Yurdugül (2019)</a> invented an _Intelligent Intervention System_ where students get feedback for each assessment - Students emphasized the usefulness of the system - <a id='cite-Iver2019'></a><a href='#bib-Iver2019'>Mac Iver, Stein, Davis, Balfanz, and Fox (2019)</a> could not find an effect from their early warning system in the ninth grade - <a id='cite-Edmunds2002'></a><a href='#bib-Edmunds2002'>Edmunds and Tancock (2002)</a> analyzed the effects of incentives on third and four-graders' reading motivation and did not find an effect ] -- <br> .blockquote[ - The literature on the effects of warning system is inconclusive - Many studies analyzed the system with questionnaires .padding_left_2[<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#004c93;" xmlns="http://www.w3.org/2000/svg"> <path d="M256 8c137 0 248 111 248 248S393 504 256 504 8 393 8 256 119 8 256 8zm-28.9 143.6l75.5 72.4H120c-13.3 0-24 10.7-24 24v16c0 13.3 10.7 24 24 24h182.6l-75.5 72.4c-9.7 9.3-9.9 24.8-.4 34.3l11 10.9c9.4 9.4 24.6 9.4 33.9 0L404.3 273c9.4-9.4 9.4-24.6 0-33.9L271.6 106.3c-9.4-9.4-24.6-9.4-33.9 0l-11 10.9c-9.5 9.6-9.3 25.1.4 34.4z"></path></svg> We try to measure the direct effect on students' performance] ] ??? just short -> inconclusive main Point --- name: RDD # RDD Toy Example — I ## Parametric Estimation <br> <img src="data:image/png;base64,#plots/late_tikz1.png" width="80%" style="display: block; margin: auto;" /> --- # RDD Toy Example — II ## Non-parametric Estimation <img src="data:image/png;base64,#plots/non_p_late_tikz1.png" width="80%" style="display: block; margin: auto;" /> -- - We used the data-driven approach by <a id='cite-imbensoptimal2009'></a><a href='#bib-imbensoptimal2009'>Imbens and Kalyanaraman (2009)</a> to determine the bandwidth ??? - The method fits the bandwidth as widely as possible without introducing other confounding effects --- # Model Assumptions <!-- - The running variable `\(W\)` does not (predicted probability to pass the exam) needs to be continuous around the cutoff, otherwise students could manipulate the treatment --> - The running variable `\(W\)` (predicted probability to pass the exam) must not have a jump around the cutoff in the density function .pull-left-2[ <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#plots/test_cont_label.png" alt="Graphical illustration of the McCrary sorting test" width="80%" /> <p class="caption">Graphical illustration of the McCrary sorting test</p> </div> ] -- .pull-right-1[ <br> - There is no jump in the density around the cutoff point of `\(0.4\)` - `\(p\)`-value: `\(0.509\)` - The incentive to manipulate the treatment is quite low ] -- .pull-down[ - Also, standard IV estimation assumptions must hold ] --- name: results # Empirical Results — I .pull-left-2[ <br> <div class="figure" style="text-align: center"> <img src="data:image/png;base64,#plots/model_plot_label.png" alt="Graphical illustration of the RDD model" width="95%" /> <p class="caption">Graphical illustration of the RDD model</p> </div> ] -- .pull-right-1[ ### Estimate - LATE: 0.193 - SE: 4.889 - `\(p\)`-value: 0.968 - Bandwidth: 0.255 - `\(N\)`: 126 ] ??? - theoretisch sollten Sie jetzt einen großen Sprung sehen - covariates auch getestet - other approaches were used --- # Empirical Results — II - The LATE estimate is positive but not significant - An estimate of `\(0.193\)` means that students who received the warning email achieved `\(0.193\)` points more than comparable students who did not - Compared to the `\(60\)`-point exam, the effect size seems limited -- - Bandwidth of `\(0.255\)` - Only students with a predicted probability `\(0.4\)` (cutoff) `\(\pm \ 0.255\)` (bandwidth), are included in the analysis -- - This leads to the effective sample size of `\(126\)` students --- name: discussion # Discussion — I - Our RDD results do not provide evidence that the warning email has a significant effect on students' results (or behavior) - The variance around the cutoff is relatively high, which compromises the detection of an effect - Many individuals are not included in the final analysis for several reasons - Students dropping the course - Students far away from the cutoff are not providing much information .padding_left_2[<svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#004c93;" xmlns="http://www.w3.org/2000/svg"> <path d="M256 8c137 0 248 111 248 248S393 504 256 504 8 393 8 256 119 8 256 8zm-28.9 143.6l75.5 72.4H120c-13.3 0-24 10.7-24 24v16c0 13.3 10.7 24 24 24h182.6l-75.5 72.4c-9.7 9.3-9.9 24.8-.4 34.3l11 10.9c9.4 9.4 24.6 9.4 33.9 0L404.3 273c9.4-9.4 9.4-24.6 0-33.9L271.6 106.3c-9.4-9.4-24.6-9.4-33.9 0l-11 10.9c-9.5 9.6-9.3 25.1.4 34.4z"></path></svg> Thus precise estimation of the treatment becomes more difficult] --- # Discussion — II - Students also get feedback through their online tests - The warning may also lead weak students to postpone participation to a later semester - The cost in our program to postpone exams is quite low - The objective feedback and motivation from one warning email is rather small --- name: f_reaserach # Further Research - The effect on the dropout rate from such warning emails or systems requires further attention - An automatic repeated feedback system could have a more significant impact on student's motivation - Detailed recurring feedback could also be used to guide students -- <br> .blockquote[ We see the open and transparent communication of the student's performance to the students as a positive aspect of the system ] --- name: references # References .font80[ Arnold, K. E. and M. Pistilli (2012). "Course signals at Purdue: using learning analytics to increase student success". Eng. In: _ACM International Conference Proceeding Series_. LAK '12. ACM, pp. 267-270. Bañeres, D., M. E. Rodríguez, A. E. Guerrero-Roldán, et al. (2020). "An Early Warning System to Detect At-Risk Students in Online Higher Education". In: _Applied Sciences_ 10.13, p. 4427. Edmunds, K. and S. M. Tancock (2002). "Incentives: The effects on the reading motivation of fourth‐grade students". In: _Reading Research and Instruction_ 42.2, pp. 17-37. Imbens, G. and K. Kalyanaraman (2009). "Optimal Bandwidth Choice for the Regression Discontinuity Estimator". In: _National Bureau of Economic Research_ 1.14726. Mac Iver, M. A., M. L. Stein, M. H. Davis, et al. (2019). "An Efficacy Study of a Ninth-Grade Early Warning Indicator Intervention". In: _Journal of Research on Educational Effectiveness_ 12.3, pp. 363-390. Şahin, M. and H. Yurdugül (2019). "An intervention engine design and development based on learning analytics: the intelligent intervention system (In 2 S)". In: _Smart Learning Environments_ 6.1, p. 18. ]